Aims and Hypotheses

Aim 1: Determine the prevalence and differentiating features of patients with versus without aPTT-anti-Xa discordance among patients receiving ECMO at CUIMC in 2024.

Hypothesis 1: Discordance is a relatively common, yet under appreciated phenomenon, and varies by geographic location of care and patient attributes including demographics, severity of illness, and underlying disease process.

Aim 2: Determine the implications of discordance between aPTT and anti-Xa on hemorrhagic and thrombotic complications in a cohort of patients who received ECMo at CUIMC in 2024.

Hypothesis 2: Discordance between aPTT and anti-Xa indicates the presence of coagulation system pathophysiology which is a risk factor for development of hemorrhage and thrombosis.

Classic systemic anticoagulation goals, anti-Xa vs aPTT, at NYP:

0.3 - 0.7 vs 51 - 83

Typical anti-Xa goal vs aPTT goals among heparinized ECMO patients:

0.1 - 0.3 vs 35 - 50 (LOW GOAL)
0.2 - 0.5 vs 45 - 65 (CLASSIC GOAL)

Methods:

Only first ECMO run used.

ECMO duration: Dr. Connie Nguyen manually reviewed heparin-exposed patients’ EMRs in June 2025 to identify true ECMO time off, as initial data review identified some labs that were drawn while on heparin but after the patient was off ECMO. The variable “time_off” in the original data was obtained from ELSO sources. Per Dr. Nguyen’s review, if her evaluation of ECMO stop time was within 4 hours of ELSO-derived time_off, then the actual true time off was deemed as that known by ELSO. If there was a greater than 4 hour discrepancy, a new time_off_true value was recorded.

Lab cutoffs:

if aPTT <20.0 - made 0
if aPTT >180 - made 180
if anti-Xa <0.04 or <0.1 - made 0
if anti-Xa >2.00 - made 2.0
if data is duplicated at a specific timepoint (e.g. 2 lab samples, exact same result time but different values), then taking the mean of the two exact same-timed values

Hyperbilirubinemia can artifactually elevate anti-Xa (colorimetric assay). Jaundice threshold is 2 - 3 mg/dL total bilirubin. Upper limit of normal total bilirubin at NYP-CUIMC is 1.2 mg/dL.

need to redo all analyses, starting with run_labs_complications_raw, checking to ensure counts are correct, now that dataframe includes true ECMO stop times

Tidy Data, Heparin exposed

count of distinct MRNs of patients exposed to heparin
n
166

counts of MRNs of patients exposed to heparin by location; counts between distinct MRNs and department_name may not match as some patients move between departments
department_name	n
MIL 5 CTICU	76
MIL 5 CCU	41
MIL 4 MICU A	31
HRT CARDIAC CARE	26
MIL OPERATING ROOM	5
MIL 4 SICU	1
MIL CARDIAC CATH	1
GBG 4 W CT ICU	1
MIL 9 HUDSON	1
MIL 5 GARDEN NORTH	1

Discordance Data, Heparin exposed

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `results = map(data, group_chisq)`.
## ℹ In group 8: `concordance_category = high aPTT low antiXa`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

concordance_category	classic	low	statistic	p.value	method
concordant low	250	72	98.398	0.000	Chi-squared test for given probabilities
low aPTT	141	139	0.014	0.905	Chi-squared test for given probabilities
concordant	133	173	5.229	0.022	Chi-squared test for given probabilities
high antiXa	7	47	29.630	0.000	Chi-squared test for given probabilities
concordant high	14	93	58.327	0.000	Chi-squared test for given probabilities
high aPTT	30	52	5.902	0.015	Chi-squared test for given probabilities
low antiXa	13	12	0.040	0.841	Chi-squared test for given probabilities
high aPTT low antiXa	1	1	0.000	1.000	Chi-squared test for given probabilities
low aPTT high antiXa	1	1	0.000	1.000	Chi-squared test for given probabilities

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `results = map(data, group_chisq)`.
## ℹ In group 4: `concordance = high aPTT low antiXa`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

count and chi-square count data hypothesis testing for concordant LABS
concordance	classic	low	statistic	p.value	method
concordant	794	676	9.472	0.002	Chi-squared test for given probabilities
discordant aPTT below antiXa	296	372	8.647	0.003	Chi-squared test for given probabilities
discordant aPTT above antiXa	86	128	8.243	0.004	Chi-squared test for given probabilities
high aPTT low antiXa	2	2	0.000	1.000	Chi-squared test for given probabilities
low aPTT high antiXa	2	2	0.000	1.000	Chi-squared test for given probabilities

count of distinct MRNs receiving heparin with time-matched aPTT and anti-Xa
n
115

counts of MRNs by location; counts between distinct MRNs and department_name may not match as some patients move between departments
department_name	n
MIL 5 CTICU	56
MIL 5 CCU	31
HRT CARDIAC CARE	23
MIL 4 MICU A	7
MIL 4 SICU	1
MIL OPERATING ROOM	1
MIL CARDIAC CATH	1
GBG 4 W CT ICU	1
MIL 9 HUDSON	1
MIL 5 GARDEN NORTH	1

counts of labs in each location
department_name	n
MIL 5 CTICU	227
MIL 5 CCU	202
HRT CARDIAC CARE	140
MIL 4 MICU A	11
GBG 4 W CT ICU	3
MIL 5 GARDEN NORTH	3
MIL 4 SICU	1
MIL OPERATING ROOM	1
MIL CARDIAC CATH	1
MIL 9 HUDSON	1

Discordance and Complications, Heparin Exposed

Demographics

basic demographics of discordant groups, each mrn with one group only
discordance_group	n
concordant	17
discordant aPTT above antiXa	22
discordant aPTT below antiXa	57
mixed	19

counts of support type by discordant groups, each mrn with one group only
name	concordant	discordant aPTT above antiXa	discordant aPTT below antiXa	mixed
n	17.0	21.0	56.0	19.0
median_age	54.0	56.0	59.5	56.0
iqr_age	30.0	24.0	20.2	16.5
male_pct	58.8	57.1	71.4	52.6
median_wt	76.0	83.2	87.8	88.5
iqr_wt	21.5	26.6	29.6	29.6
median_ht	170.0	170.0	170.7	165.1
iqr_ht	10.1	15.8	12.8	15.2
median_hours	64.0	124.0	119.5	242.0
iqr_hours	107.0	119.0	117.8	175.0

discordance_group	n	pct_Cardiac	pct_Pulmonary	pct_ECPR
concordant	10	58.8	0.0	0.0
concordant	7	0.0	41.2	0.0
discordant aPTT above antiXa	16	76.2	0.0	0.0
discordant aPTT above antiXa	2	0.0	0.0	9.5
discordant aPTT above antiXa	3	0.0	14.3	0.0
discordant aPTT below antiXa	38	67.9	0.0	0.0
discordant aPTT below antiXa	3	0.0	0.0	5.4
discordant aPTT below antiXa	15	0.0	26.8	0.0
mixed	16	84.2	0.0	0.0
mixed	1	0.0	0.0	5.3
mixed	2	0.0	10.5	0.0

## Warning: There were 12 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `results = map(data, group_chisq_complications_once)`.
## ℹ In group 5: `hypothetical_treatment = "classic"` and `complication =
##   "hemorrhagic_peripheral_cannulation_site_bleeding"`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 11 remaining warnings.

## # A tibble: 24 × 11
## # Groups:   hypothetical_treatment, complication [24]
##    complication       hypothetical_treatment statistic  p.value method data.name
##    <chr>              <chr>                      <dbl>    <dbl> <chr>  <chr>    
##  1 death_date         classic                     2.62  0.455   Chi-s… <NA>     
##  2 death_date         low                        11.8   0.00793 Chi-s… <NA>     
##  3 metabolic_hyperbi… classic                     0.4   0.819   Chi-s… <NA>     
##  4 metabolic_hyperbi… low                         0.4   0.819   Chi-s… <NA>     
##  5 mechanical_circui… classic                     0     1       Chi-s… <NA>     
##  6 mechanical_circui… low                         0     1       Chi-s… <NA>     
##  7 hemorrhagic_perip… classic                     0     1       Chi-s… <NA>     
##  8 hemorrhagic_perip… low                         0     1       Chi-s… <NA>     
##  9 neurologic_cns_in… classic                    NA    NA       Only … <NA>     
## 10 neurologic_cns_in… low                        NA    NA       Only … <NA>     
## # ℹ 14 more rows
## # ℹ 5 more variables: concordant <int>, `discordant aPTT above antiXa` <int>,
## #   `discordant aPTT below antiXa` <int>, mixed <int>,
## #   `low aPTT high antiXa` <int>

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `results = map(data, group_chisq_complications_once)`.
## ℹ In group 2: `hypothetical_treatment = "classic"` and
##   `hemorrhage_thrombosis_death = "hemorrhage_or_thrombosis"`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

analysis based on counting each patient once, focused on composite of hemorrhagic + thrombotic complications (mechanical circuit change, hemorrhagic peripheral site bleeding, hemorrhagic GI bleeding, hemorrhagic surgical site bleeding) OR death
hypothetical_treatment	hemorrhage_thrombosis_death	statistic	p.value	method	concordant	discordant aPTT above antiXa	discordant aPTT below antiXa	mixed
classic	death	2.6153846	0.4547988	Chi-squared test for given probabilities	9	4	8	5
low	death	11.8461538	0.0079292	Chi-squared test for given probabilities	5	4	14	3
classic	hemorrhage_or_thrombosis	1.5714286	0.6658854	Chi-squared test for given probabilities	3	1	1	2
low	hemorrhage_or_thrombosis	0.4285714	0.9342791	Chi-squared test for given probabilities	2	2	2	1

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `results = map(data, group_chisq_complications_once)`.
## ℹ In group 2: `hypothetical_treatment = "classic"` and
##   `hemorrhage_thrombosis_death = "hemorrhage_or_thrombosis"`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

analysis based on counting each patient once, focused on composite of hemorrhagic + thrombotic complications (mechanical circuit change, hemorrhagic peripheral site bleeding, hemorrhagic GI bleeding, hemorrhagic surgical site bleeding) OR death AND filtering out patients with hyperbilirubinemia > 2.5 mg/dL (which may increase anti-Xa artifactually
hypothetical_treatment	hemorrhage_thrombosis_death	statistic	p.value	method	concordant	discordant aPTT above antiXa	discordant aPTT below antiXa	mixed
classic	death	2.6153846	0.4547988	Chi-squared test for given probabilities	9	4	8	5
low	death	11.8461538	0.0079292	Chi-squared test for given probabilities	5	4	14	3
classic	hemorrhage_or_thrombosis	1.5714286	0.6658854	Chi-squared test for given probabilities	3	1	1	2
low	hemorrhage_or_thrombosis	0.4285714	0.9342791	Chi-squared test for given probabilities	2	2	2	1

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `results = map(data, group_chisq_complications_once)`.
## ℹ In group 2: `hypothetical_treatment = "classic"` and
##   `hemorrhage_thrombosis_death = "hemorrhage_or_thrombosis"`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

analysis based on counting each patient once, focused on composite of hemorrhagic + thrombotic complications (mechanical circuit change, hemorrhagic peripheral site bleeding, hemorrhagic GI bleeding, hemorrhagic surgical site bleeding) OR death with all discordance grouped together
hypothetical_treatment	hemorrhage_thrombosis_death	statistic	p.value	method	concordant	discordant
classic	death	2.4615385	0.1166645	Chi-squared test for given probabilities	9	17
low	death	9.8461538	0.0017019	Chi-squared test for given probabilities	5	21
classic	hemorrhage_or_thrombosis	0.1428571	0.7054570	Chi-squared test for given probabilities	3	4
low	hemorrhage_or_thrombosis	1.2857143	0.2568393	Chi-squared test for given probabilities	2	5

Polynomial Fitting

# Fit mixed-effects model with random intercept for patient (mrn)
discordance_model <- lmer(
  heparin_assay_quantitative ~ poly(activated_partial_thromboplastin_time, 2) + (1 | mrn),
  data = discordance_data)

# Create prediction grid over the range of predictor
new_data <- data.frame(
  activated_partial_thromboplastin_time = seq(
    min(discordance_data$activated_partial_thromboplastin_time, na.rm = TRUE),
    max(discordance_data$activated_partial_thromboplastin_time, na.rm = TRUE),
    length.out = 300  ),
  mrn = discordance_data$mrn[1]  # Assign any existing patient ID for prediction
)

pred_interval <- merTools::predictInterval(
  discordance_model,          # model as first argument, no name
  newdata = new_data,
  level = 0.99,
  n.sims = 1000,
  which = "fixed",
  include.resid.var = TRUE)

# Bind predictions to the new_data
new_data <- bind_cols(new_data, pred_interval)

# Flag unusual data points outside prediction intervals
discordance_data =
  discordance_data %>%
  rowwise() %>%
  mutate(
    # Find closest prediction row by activated_partial_thromboplastin_time
    pred_row = list(new_data[which.min(abs(activated_partial_thromboplastin_time - new_data$activated_partial_thromboplastin_time)), ]),
    predicted = pred_row$fit,
    lower = pred_row$lwr,
    upper = pred_row$upr,
    unusual = heparin_assay_quantitative < lower | heparin_assay_quantitative > upper
  ) %>%
  ungroup()

# Plot predictions with prediction intervals and flagged unusual points
ggplot() +
  geom_ribbon(data = new_data,
              aes(x = activated_partial_thromboplastin_time, ymin = lwr, ymax = upr),
              fill = "blue", alpha = 0.2) +
  geom_line(data = new_data,
            aes(x = activated_partial_thromboplastin_time, y = fit),
            color = "blue") +
  geom_point(data = discordance_data,
             aes(x = activated_partial_thromboplastin_time, y = heparin_assay_quantitative, color = unusual)) +
  scale_color_manual(values = c("black", "red")) +
  labs(
    color = "Unusual",
    title = "Mixed Model Fit with 99% Prediction Interval",
    x = "Activated Partial Thromboplastin Time",
    y = "Heparin Assay Quantitative"
  ) +
  theme_minimal()

discordance_data

## # A tibble: 1,180 × 17
##    mrn   patient_name lab_result_time     department_name heparin_infusion_dos…¹
##    <chr> <chr>        <dttm>              <fct>           <chr>                 
##  1 1000… AHMED, AMENA 2024-11-03 23:47:00 MIL 5 CCU       5                     
##  2 1000… AHMED, AMENA 2024-11-03 23:47:00 MIL 5 CCU       5                     
##  3 1000… AHMED, AMENA 2024-11-04 06:41:00 MIL 5 CCU       5                     
##  4 1000… AHMED, AMENA 2024-11-04 06:41:00 MIL 5 CCU       5                     
##  5 1000… AHMED, AMENA 2024-11-04 15:35:00 MIL 5 CCU       5                     
##  6 1000… AHMED, AMENA 2024-11-04 15:35:00 MIL 5 CCU       5                     
##  7 1000… AHMED, AMENA 2024-11-04 19:18:00 MIL 5 CCU       5                     
##  8 1000… AHMED, AMENA 2024-11-04 19:18:00 MIL 5 CCU       5                     
##  9 1000… AHMED, AMENA 2024-11-05 06:44:00 MIL 5 CCU       5                     
## 10 1000… AHMED, AMENA 2024-11-05 06:44:00 MIL 5 CCU       5                     
## # ℹ 1,170 more rows
## # ℹ abbreviated name: ¹heparin_infusion_dose_value_units_kg_hr
## # ℹ 12 more variables: concordance_classic_simple <fct>,
## #   concordance_low_simple <fct>, discordance_group <fct>,
## #   hypothetical_treatment <chr>, concordance_category <fct>,
## #   activated_partial_thromboplastin_time <dbl>,
## #   heparin_assay_quantitative <dbl>, pred_row <list>, predicted <dbl>, …

Note that patients can count in multiple renal failure categories. I.e. Patient X is included in renal Cr 1.5 - 3.0 AND Cr > 3.0.

We need thrombotic complications:

DVT

We need additional hemorrhagic complications:

RBCs
FFP
platelet

What are the actual ranges of aPTT and anti-Xa among those with completely concordant versus discordant categories? Why are there different death outcomes? Are the demographics different? Disease processes?

2024_anticoagulation

Aaron Mittel

2025-07-22